Institute of Computing Technology, Chinese Academy IR
| Bilevel Multiview Latent Space Learning | |
| Xue, Zhe1,2; Li, Guorong1,2; Wang, Shuhui3; Zhang, Weigang4,5; Huang, Qingming1,2,3 | |
| 2018-02-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| ISSN | 1051-8215 |
| 卷号 | 28期号:2页码:327-341 |
| 摘要 | Different kinds of features describe different aspects of image data, and each feature can be treated as a view when we take it as a particular understanding of images. Leveraging multiple views provides a richer and comprehensive description than using only a single view. However, multiview data are often represented by high-dimensional heterogeneous features, so it is meaningful to find a low-dimensional consensus representation from multiple views. In this paper, we propose an unsupervised multiview dimensionality reduction method for images based on bilevel latent space learning. As different views have different physical meanings and statistical properties, they are not directly comparable. Therefore, we learn the comparable representation for each view in the first level. The shared and the private nature of multiview data are exploited to accurately preserve the information of each view. Then, we fuse different views into a low-dimensional representation by conducting joint matrix factorization in the second level. To guarantee the low-dimensional representation to be compact and discriminative, the intrinsic geometric structure of data is utilized. Besides, our method considers resisting the outliers and noise contained in multiview data, which may influence the learned representation and deteriorate its semantic consistency. We design appropriate optimization objectives to learn the latent spaces in different levels. Compared with the existing methods, our method could provide a more flexible multiview learning strategy that not only accurately captures the information of each view but also is robust to outliers and noise, which can obtain a more discriminative and compact low-dimensional representation. Experiments on two real-world image data sets demonstrate the advantages of our method over the existing multiview dimensionality reduction methods. |
| 关键词 | Image and video classification latent space matrix factorization multiview |
| DOI | 10.1109/TCSVT.2016.2607842 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB351802] ; National Natural Science Foundation of China[61303153] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61672497] ; 863 program of China[2014AA015202] ; Bureau of Frontier Sciences and Education (CAS)[QYZDJ-SSW-SYS013] |
| WOS研究方向 | Engineering |
| WOS类目 | Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000425036400005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.204/handle/2XEOYT63/5626 |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Guorong; Huang, Qingming |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China 2.UCAS, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China 5.Univ Chinese Acad Sci, Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xue, Zhe,Li, Guorong,Wang, Shuhui,et al. Bilevel Multiview Latent Space Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2018,28(2):327-341. |
| APA | Xue, Zhe,Li, Guorong,Wang, Shuhui,Zhang, Weigang,&Huang, Qingming.(2018).Bilevel Multiview Latent Space Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,28(2),327-341. |
| MLA | Xue, Zhe,et al."Bilevel Multiview Latent Space Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28.2(2018):327-341. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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